Publications | Yongjian Fu

2026

ICLR’26

RAG4DMC: Retrieval-Augmented Generation for Data-Level Modality Completion

Ningxin He , Yongheng Deng , Sheng Yue , and 3 more authors

In The Fourteenth International Conference on Learning Representations , 2026

@inproceedings{anonymous2026ragdmc,
  author = {He, Ningxin and Deng, Yongheng and Yue, Sheng and Fu, Yongjian and Zhang, Zehui and Tiegang, Gao},
  title = {{RAG}4{DMC}: Retrieval-Augmented Generation for Data-Level Modality Completion},
  booktitle = {The Fourteenth International Conference on Learning Representations},
  year = {2026},
  url = {https://openreview.net/forum?id=6LA7KDjNsy},
}

TMC’26

Mobile and Multi-Device Wireless Charging

Lili Chen , Bozhong Yu , Yongjian Fu, and 5 more authors

IEEE Transactions on Mobile Computing, 2026

Bib

@article{ChargeX,
  author = {Chen, Lili and Yu, Bozhong and Fu, Yongjian and Ren, Ju and Pan, Hao and Gummeson, Jeremy and Wang, Ling and Zhang, Yaoxue},
  journal = {IEEE Transactions on Mobile Computing},
  title = {Mobile and Multi-Device Wireless Charging},
  year = {2026},
}

INFOCOM’26

EarAuth: Towards Practical Cardiac Vibration Authentication on COTS Wireless Earbuds

Yongjian Fu, Wenpeng Zhu , Yingjun Wu , and 8 more authors

In Proceedings of the 46th Annual IEEE International Conference on Computer Communications , May 2026

Bib

@inproceedings{INFOCOM26_EarAuth,
  author = {Fu, Yongjian and Zhu, Wenpeng and Wu, Yingjun and Pan, Hao and Zhao, Yizhe and Wang, Guanbo and Zhang, Jinrui and Li, Li and Deng, Yongheng and Zhang, Yaoxue and Ren, Ju},
  title = {EarAuth: Towards Practical Cardiac Vibration Authentication on COTS Wireless Earbuds},
  booktitle = {Proceedings of the 46th Annual IEEE International Conference on Computer Communications},
  series = {IEEE INFOCOM},
  year = {2026},
  address = {Tokyo, Japan},
  month = may,
}

MobiCom’26

Unlocking Practical Cardiac Monitoring Capabilities on True Wireless Stereo Earbuds

Yongjian Fu, Ke Sun , Xinyu Zhang , and 5 more authors

In Proceedings of the 32nd Annual International Conference on Mobile Computing and Networking , Nov 2026

Bib

@inproceedings{MobiCom26_TWSEarbuds,
  author = {Fu, Yongjian and Sun, Ke and Zhang, Xinyu and Wang, Ruyao and Li, Xinyi and Pan, Hao and Zhang, Yaoxue and Ren, Ju},
  title = {Unlocking Practical Cardiac Monitoring Capabilities on True Wireless Stereo Earbuds},
  booktitle = {Proceedings of the 32nd Annual International Conference on Mobile Computing and Networking},
  series = {ACM MobiCom},
  year = {2026},
  address = {Austin, USA},
  month = nov,
}

MobiCom’26

MANA: Towards Efficient Mobile Ad Detection via Multimodal Agentic UI Navigation

Yizhe Zhao , Yongjian Fu*, Zihao Feng , and 4 more authors

In Proceedings of the 32nd Annual International Conference on Mobile Computing and Networking , Nov 2026

(* Corresponding author)

Bib

@inproceedings{MobiCom26_MANA,
  author = {Zhao, Yizhe and Fu*, Yongjian and Feng, Zihao and Pan, Hao and Deng, Yongheng and Zhang, Yaoxue and Ren, Ju},
  title = {MANA: Towards Efficient Mobile Ad Detection via Multimodal Agentic UI Navigation},
  booktitle = {Proceedings of the 32nd Annual International Conference on Mobile Computing and Networking},
  series = {ACM MobiCom},
  year = {2026},
  address = {Austin, USA},
  month = nov,
  note = {(* Corresponding author)},
}

MobiCom’26

Attacking mmWave Imaging Using Metasurfaces Based on Intensity and Time-of-Flight Manipulation

Shuning Wang , Hao Pan , Yongjian Fu*, and 7 more authors

In Proceedings of the 32nd Annual International Conference on Mobile Computing and Networking , Nov 2026

(* Corresponding author)

Bib

@inproceedings{MobiCom26_MMWaveAttack,
  author = {Wang, Shuning and Pan, Hao and Fu*, Yongjian and Li, Xinyi and Zhong, Linghui and Xie, Yaxiong and Sun, Ke and Xue, Guangtao and Zhang, Yaoxue and Ren, Ju},
  title = {Attacking mmWave Imaging Using Metasurfaces Based on Intensity and Time-of-Flight Manipulation},
  booktitle = {Proceedings of the 32nd Annual International Conference on Mobile Computing and Networking},
  series = {ACM MobiCom},
  year = {2026},
  address = {Austin, USA},
  month = nov,
  note = {(* Corresponding author)},
}

AES’26

A Lightweight Neural Speech Compression Method for Edge Devices

Yu Lu , Yongjian Fu*, Hao Pan , and 3 more authors

Acta Electronica Sinica, Feb 2026

(* Corresponding author)

Bib

@article{AES26_SpeechCompression,
  author = {Lu, Yu and Fu*, Yongjian and Pan, Hao and Ding, Dian and Xue, Guangtao and Ren, Ju},
  title = {A Lightweight Neural Speech Compression Method for Edge Devices},
  journal = {Acta Electronica Sinica},
  year = {2026},
  month = feb,
  note = {(* Corresponding author)},
}

2025

SA’25

MODepth: Benchmarking Multi-frame Monocular Depth Estimation with Optical Image Stabilization

Yu Lu , Dian Ding , Hao Pan , and 5 more authors

In ACM SIGGRAPH Asia , Dec 2025

Bib

@inproceedings{SIGGRAPHAsia25_MODepth,
  author = {Lu, Yu and Ding, Dian and Pan, Hao and Ding, Jiatong and Fu, Yongjian and Chen, Yi{-}Chao and Ren, Ju and Xue, Guangtao},
  title = {MODepth: Benchmarking Multi-frame Monocular Depth Estimation with Optical Image Stabilization},
  booktitle = {ACM SIGGRAPH Asia},
  year = {2025},
  address = {Hong Kong, China},
  month = dec,
}

MobiCom’25
WDNN: Weighted Diffractive Neural Network for Physical-layer RF Signal Processing

Yezhou Wang^ , Yongjian Fu^, Hao Pan , and 5 more authors

In Proceedings of the 31st Annual International Conference on Mobile Computing and Networking , Nov 2025

(^ Co-primary authors)

Abs Bib

Diffractive neural networks (NNs) have garnered attention for directly implementing wireless signal processing at the physical layer. However, they are limited by a constrained weight learning space and activation functions, which restricts their data processing capabilities. To address this, we propose an RF circuit-based weighted diffraction NN (WDNN) that rivals digital NNs in processing ability. We design a weighted asymmetric RF coupler unit that, when stacked into a network, enables diffractive propagation with arbitrary connection weights. Additionally, an activation module is introduced that utilizes RF amplifiers operating in their nonlinear regions. We validate the effectiveness of the proposed WDNN through three tasks: 32-level amplitude modulated (AM) signal decoding, 31-class angle of arrival (AoA) estimation, and 2-class Wi-Fi based fall detection. After training, WDNN achieves the accuracy of 98.5%, 93.7%, and 90.8% in the AM decoding, AoA estimation, and fall detection tasks, respectively; while the diffractive NN SOTA achieves only 21.6%, 16.9%, and 63.3%. We also implement the prototypes of WDNN and SOTA, and real-world experimental results demonstrate that our method achieves an average accuracy improvement of up to 76.85% across various tasks compared to SOTA.
@inproceedings{WDNN_yezhou, author = {Wang^, Yezhou and Fu^, Yongjian and Pan, Hao and Hu, Qinyun and Qiu, Lili and Chen, Yi-Chao and Xue, Guangtao and Ren, Ju}, title = {WDNN: Weighted Diffractive Neural Network for Physical-layer RF Signal Processing}, year = {2025}, isbn = {9798400711299}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3680207.3765240}, doi = {10.1145/3680207.3765240}, booktitle = {Proceedings of the 31st Annual International Conference on Mobile Computing and Networking}, pages = {697–711}, numpages = {15}, keywords = {diffractive neural network, signal processing, radio-frequency hardware}, location = {Kerry Hotel, Hong Kong, Hong Kong, China}, series = {ACM MOBICOM '25}, month = nov, note = {(^ Co-primary authors)}, }
UIST’25
UltraPoser: Pushing the Limits of IMU-based Full-Body Pose Estimation with Ultrasound Sensing on Consumer Wearables

Yadong Li , Shuning Wang , Yongjian Fu, and 6 more authors

In Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology , Sep 2025

Abs Bib

Full-body motion capture using IMUs embedded in consumer wearables has the potential to enable convenient, on-the-go tracking with minimal instrumentation. However, the sparse placement of these devices on the body frame presents challenges such as limited body coverage, reduced motion feature diversity, and cumulative drift errors. This paper introduces UltraPoser, a multi-modal full-body motion capture system that integrates ultrasonic sensing with inertial measurements for improved fidelity, broader coverage and increased reliability. UltraPoser leverages built-in microphones and speakers on commodity wearables, such as smartphones and smartwatches, to transmit and receive inaudible ultrasound signals, expanding the range of sensed body areas and providing drift-free acoustic multipath profiles. To implement UltraPoser, we systematically explore ultrasound signal designs to maximize feature quality and propose a graph-based physics-aware fusion architecture to integrate heterogeneous sensing modalities. We evaluate our approach using the UltraPoser Dataset, collected from 10 participants across diverse device placements and activity contexts. Compared to state-of-the-art IMU-only methods, UltraPoser achieves a 28.46% improvement in overall pose estimation accuracy and up to 67.28% error reduction for specific limbs without directly attached sensors.
@inproceedings{10.1145/3746059.3747714, author = {Li, Yadong and Wang, Shuning and Fu, Yongjian and Chen, Justin and Chen, Xingyu and Ren, Ju and Zhang, Xinyu and Gadre, Akshay and Sun, Ke}, title = {UltraPoser: Pushing the Limits of IMU-based Full-Body Pose Estimation with Ultrasound Sensing on Consumer Wearables}, year = {2025}, isbn = {9798400720376}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3746059.3747714}, doi = {10.1145/3746059.3747714}, booktitle = {Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology}, articleno = {93}, numpages = {15}, month = sep, keywords = {Motion capture, ultrasound sensing, multi-modal fusion}, location = { }, series = {UIST '25}, }

TDSC’25

MoiréComm: Secure Screen-Camera Communication Based on Moiré Cryptography

Hao Pan , Yongjian Fu*, Yu Lu , and 3 more authors

IEEE Transactions on Dependable and Secure Computing, Aug 2025

(* Corresponding author)

Bib

@article{Moirecomm_haopan,
  author = {Pan, Hao and Fu*, Yongjian and Lu, Yu and Tan, Feitong and Chen, Yi-Chao and Ren, Ju},
  journal = {IEEE Transactions on Dependable and Secure Computing},
  title = {MoiréComm: Secure Screen-Camera Communication Based on Moiré Cryptography},
  year = {2025},
  volume = {22},
  number = {6},
  pages = {7552-7570},
  keywords = {QR codes;Cameras;Codes;Encryption;Security;Decoding;Online banking;Protocols;Passwords;Operating systems;Screen-camera communication;secure QR code;nonlinearity},
  doi = {10.1109/TDSC.2025.3598300},
  month = aug,
  note = {(* Corresponding author)},
}

TMC’25

MASA: Multimodal Federated Learning Through Modality-Aware and Secure Aggregation

Jialin Guo , Yongjian Fu*, Zhiwei Zhai , and 6 more authors

IEEE Transactions on Mobile Computing, Aug 2025

(* Corresponding author)

Bib

@article{MASA_guo,
  author = {Guo, Jialin and Fu*, Yongjian and Zhai, Zhiwei and Li, Xinyi and Deng, Yongheng and Yue, Sheng and Chen, Lili and Pan, Hao and Ren, Ju},
  journal = {IEEE Transactions on Mobile Computing},
  title = {MASA: Multimodal Federated Learning Through Modality-Aware and Secure Aggregation},
  year = {2025},
  volume = {24},
  number = {8},
  pages = {7328-7344},
  keywords = {Federated learning;Data models;Servers;Data privacy;Training;Privacy;Multimodal sensors;Mobile computing;Logic gates;Performance evaluation;Multimodal sensing;federated learning;knowledge distillation},
  doi = {10.1109/TMC.2025.3548954},
  month = aug,
  note = {(* Corresponding author)},
}

MobiSys’25

Towards Distance-Adaptive Wireless Charging

Lili Chen , Yizhe Zhao , Shuning Wang , and 5 more authors

In Proceedings of the 23rd Annual International Conference on Mobile Systems, Applications and Services , Jun 2025

Bib

@inproceedings{MobiSys25_DistanceAdaptiveCharging,
  author = {Chen, Lili and Zhao, Yizhe and Wang, Shuning and Zhong, Linghui and Fu, Yongjian and Yue, Sheng and Ren, Ju and Zhang, Yaoxue},
  title = {Towards Distance-Adaptive Wireless Charging},
  booktitle = {Proceedings of the 23rd Annual International Conference on Mobile Systems, Applications and Services},
  series = {ACM MobiSys},
  year = {2025},
  address = {Anaheim, California, USA},
  month = jun,
}

TMC’25

MagicWrite: One-Dimensional Acoustic Tracking-Based Air Writing System

Hao Pan , Yongjian Fu, Ye Qi , and 2 more authors

IEEE Transactions on Mobile Computing, Jan 2025

Bib

@article{MagicWrite,
  author = {Pan, Hao and Fu, Yongjian and Qi, Ye and Chen, Yi-Chao and Ren, Ju},
  journal = {IEEE Transactions on Mobile Computing},
  title = {MagicWrite: One-Dimensional Acoustic Tracking-Based Air Writing System},
  year = {2025},
  volume = {24},
  number = {5},
  pages = {4403-4418},
  keywords = {Writing;Radar tracking;Trajectory;Microphones;Tracking;Accuracy;Target tracking;Acoustics;Long short term memory;Computational modeling;Air writing system;wireless sensing;human-computer interaction},
  doi = {10.1109/TMC.2025.3526185},
  month = jan,
}

TMC’25

MagSpy: Revealing User Privacy Leakage via Magnetometer on Mobile Devices

Yongjian Fu, Lanqing Yang , Hao Pan , and 3 more authors

IEEE Transactions on Mobile Computing, Mar 2025

Bib

@article{MagSpy,
  author = {Fu, Yongjian and Yang, Lanqing and Pan, Hao and Chen, Yi-Chao and Xue, Guangtao and Ren, Ju},
  journal = {IEEE Transactions on Mobile Computing},
  title = {MagSpy: Revealing User Privacy Leakage via Magnetometer on Mobile Devices},
  year = {2025},
  volume = {24},
  number = {3},
  pages = {2455-2469},
  keywords = {Magnetometers;Mobile applications;Pins;Mobile computing;Object recognition;Electromagnetics;Noise;Magnetic devices;Continuous wavelet transforms;Accuracy;Electromagnetic signal;mobile application usage;privacy},
  doi = {10.1109/TMC.2024.3495506},
  month = mar,
}

2024

UIC’24

Unveiling Contactless Sensing with LiDAR Mobility

Junying Hu , Yongjian Fu, Lili Chen , and 4 more authors

In 2024 IEEE Smart World Congress (SWC) , Dec 2024

Best Paper Award

Bib

@inproceedings{UIC_junying,
  author = {Hu, Junying and Fu, Yongjian and Chen, Lili and Li, Xinyi and Sun, Xue and Ren, Ju and Zhang, Yaoxue},
  booktitle = {2024 IEEE Smart World Congress (SWC)},
  title = {Unveiling Contactless Sensing with LiDAR Mobility},
  year = {2024},
  volume = {},
  number = {},
  pages = {487-494},
  keywords = {Point cloud compression;Image segmentation;Laser radar;Accuracy;Motion segmentation;Interference;Robot sensing systems;Reflection;Sensors;Iterative methods;wireless sensing;robot sensing systems;respiration sensing;LiDAR},
  doi = {10.1109/SWC62898.2024.00098},
  month = dec,
  note = {<span class="note-best-paper">Best Paper Award</span>},
}

SenSys’24
M3Cam: Extreme Super-resolution via Multi-Modal Optical Flow for Mobile Cameras

Yu Lu , Dian Ding , Hao Pan , and 7 more authors

In Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems , Nov 2024

Abs Bib

The demand for ultra-high-resolution imaging in mobile phone photography is continuously increasing. However, the image resolution of mobile devices is typically constrained by the size of the CMOS sensor. Although deep learning-based super-resolution (SR) techniques have the potential to overcome this limitation, existing SR neural network models require large computational resources, making them unsuitable for real-time SR imaging on current mobile devices. Additionally, cloud-based SR systems pose privacy leakage risks. In this paper, we propose M3Cam, an innovative and lightweight SR imaging system for mobile phones. M3Cam can ensure high-quality 16\texttimes SR image (4\texttimes in both height and width) visualization with almost negligible latency. In detail, we utilize an optical image stabilization (OIS) module for lens control and introduce a new modality of data, namely gyroscope readings, to achieve high-precision and compact optical flow estimation modules. Building upon this concept, we design a multi-frame-based SR model utilizing the Swin Transformer. Our proposed system can generate a 16\texttimes SR image from four captured low-resolution images in real-time, with low computational load, low inference latency, and minimal reliance on runtime RAM. Through extensive experiments, we demonstrate that our proposed multi-modal optical flow model significantly enhances pixel alignment accuracy between multiple frames and delivers outstanding 16\texttimes SR imaging results under various shooting scenarios. Code and dataset are available at: https://github.com/liangjindeamo-yuer/M3CAM
@inproceedings{M3Cam, author = {Lu, Yu and Ding, Dian and Pan, Hao and Fu, Yongjian and Zhang, Liyun and Tan, Feitong and Wang, Ran and Chen, Yi-Chao and Xue, Guangtao and Ren, Ju}, title = {M3Cam: Extreme Super-resolution via Multi-Modal Optical Flow for Mobile Cameras}, year = {2024}, isbn = {9798400706974}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3666025.3699371}, doi = {10.1145/3666025.3699371}, booktitle = {Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems}, pages = {744–756}, numpages = {13}, keywords = {super-resolution system, optical flow, mobile camera}, location = {Hangzhou, China}, series = {SenSys'24}, month = nov, }
UIST’24
HandPad: Make Your Hand an On-the-go Writing Pad via Human Capacitance

Yu Lu , Dian Ding , Hao Pan , and 6 more authors

In Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology , Oct 2024

Abs Bib

The convenient text input system is a pain point for devices such as AR glasses, and it is difficult for existing solutions to balance portability and efficiency. This paper introduces HandPad, the system that turns the hand into an on-the-go touchscreen, which realizes interaction on the hand via human capacitance. HandPad achieves keystroke and handwriting inputs for letters, numbers, and Chinese characters, reducing the dependency on capacitive or pressure sensor arrays. Specifically, the system verifies the feasibility of touch point localization on the hand using the human capacitance model and proposes a handwriting recognition system based on Bi-LSTM and ResNet. The transfer learning-based system only needs a small amount of training data to build a handwriting recognition model for the target user. Experiments in real environments verify the feasibility of HandPad for keystroke (accuracy of 100%) and handwriting recognition for letters (accuracy of 99.1%), numbers (accuracy of 97.6%) and Chinese characters (accuracy of 97.9%).
@inproceedings{HandPad, author = {Lu, Yu and Ding, Dian and Pan, Hao and Li, Yijie and Zhou, Juntao and Fu, Yongjian and Zhang, Yongzhao and Chen, Yi-Chao and Xue, Guangtao}, title = {HandPad: Make Your Hand an On-the-go Writing Pad via Human Capacitance}, year = {2024}, isbn = {9798400706288}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3654777.3676328}, doi = {10.1145/3654777.3676328}, booktitle = {Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology}, articleno = {4}, numpages = {16}, keywords = {Handwriting Input, Human Capacitance, Key Stroke}, location = {Pittsburgh, PA, USA}, series = {UIST '24}, month = oct, }

TMC’24

UltraSR: Silent Speech Reconstruction via Acoustic Sensing

Yongjian Fu, Shuning Wang , Linghui Zhong , and 3 more authors

IEEE Transactions on Mobile Computing, Dec 2024

Bib

@article{UltraSR,
  author = {Fu, Yongjian and Wang, Shuning and Zhong, Linghui and Chen, Lili and Ren, Ju and Zhang, Yaoxue},
  journal = {IEEE Transactions on Mobile Computing},
  title = {UltraSR: Silent Speech Reconstruction via Acoustic Sensing},
  year = {2024},
  volume = {23},
  number = {12},
  pages = {12848-12865},
  keywords = {Ultrasonic imaging;Sensors;Acoustics;Task analysis;Mobile computing;Training;Tongue;Acoustic sensing;human-computer interaction;silent speech interface},
  doi = {10.1109/TMC.2024.3419170},
  month = dec,
}

MobiSys’24
Adaptive Metasurface-Based Acoustic Imaging using Joint Optimization

Yongjian Fu, Yongzhao Zhang , Yu Lu , and 7 more authors

In Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services , Jun 2024

Abs Bib

Acoustic imaging is attractive due to its ability to work under occlusion, different lighting conditions, and privacy-sensitive environments. Existing acoustic imaging methods require large transceiver arrays or device movement, which makes it challenging to use in many scenarios. In this paper, we develop a novel acoustic imaging system for low-cost devices with few speakers and microphones without any device movement. To achieve this goal, we leverage a 3D-printed passive acoustic metasurface to significantly enhance the diversity of the measurement data, thereby improving the imaging quality. Specifically, we jointly design the transmission signal, transceivers’ beamforming weights, metasurface, and imaging algorithm to minimize the imaging reconstruction error in an end-to-end manner. We further develop a scheme to dynamically adapt the imaging resolution based on the distance to the target. We implement a system prototype. Using extensive experiments, we show that our system yields high-quality images across a wide range of scenarios.
@inproceedings{MAJIC, author = {Fu, Yongjian and Zhang, Yongzhao and Lu, Yu and Qiu, Lili and Chen, Yi-Chao and Wang, Yezhou and Wang, Mei and Li, Yijie and Ren, Ju and Zhang, Yaoxue}, title = {Adaptive Metasurface-Based Acoustic Imaging using Joint Optimization}, year = {2024}, isbn = {9798400705816}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3643832.3661863}, doi = {10.1145/3643832.3661863}, booktitle = {Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services}, pages = {492–504}, numpages = {13}, keywords = {acoustic imaging, compressive sensing, joint optimization}, location = {Minato-ku, Tokyo, Japan}, series = {MOBISYS '24}, month = jun, }
MobiCom’24
Pushing Wireless Charging from Station to Travel

Lili Chen , Bozhong Yu , Yongjian Fu, and 4 more authors

In Proceedings of the 30th Annual International Conference on Mobile Computing and Networking , Nov 2024

Abs Bib

Wireless charging has achieved promising progress in recent years. However, the severe bottlenecks are the small charging range and poor flexibility. This paper presents ChargeX to enable smart and long-range wireless charging for small mobile devices. ChargeX incorporates emerging smart metasurface into the magnetic resonance coupling-based wireless charging to extend the charging range and accommodates the mobility of charging device. Unlike previous endeavors in metasurface-assisted wireless charging that focused on simulation, ChargeX makes efforts across software and hardware to meet three crucial requirements for a practical wireless charging system: (i) realize high-freedom and accurate metasurface control under the premise of low loss; (ii) obtain real-time feedback from the receiver and make effective manipulation for transmitted magnetic flux; and (iii) generate a proper AC signal source at the desired frequency band. We developed a prototype of ChargeX, and evaluated its performance through controlled experiments and real-world phone charging. Extensive experiments demonstrate the great potential of ChargeX for long-range and flexible wireless charging with a compact receiver design.
@inproceedings{ChargeY, author = {Chen, Lili and Yu, Bozhong and Fu, Yongjian and Ren, Ju and Pan, Hao and Gummeson, Jeremy and Zhang, Yaoxue}, title = {Pushing Wireless Charging from Station to Travel}, year = {2024}, isbn = {9798400704895}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3636534.3649346}, doi = {10.1145/3636534.3649346}, booktitle = {Proceedings of the 30th Annual International Conference on Mobile Computing and Networking}, location = {Washington D.C., DC, USA}, series = {ACM MobiCom '24}, month = nov, }
IMWUT’24
Pushing the Limits of Acoustic Spatial Perception via Incident Angle Encoding

Yongjian Fu, Yongzhao Zhang , Hao Pan , and 7 more authors

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., May 2024

Abs Bib

With the growing popularity of smart speakers, numerous novel acoustic sensing applications have been proposed for low-frequency human speech and high-frequency inaudible sounds. Spatial information plays a crucial role in these acoustic applications, enabling various location-based services. However, typically commercial microphone arrays face limitations in spatial perception of inaudible sounds due to their sparse array geometries optimized for low-frequency speech. In this paper, we introduce MetaAng, a system designed to augment microphone arrays by enabling wideband spatial perception across both speech signals and inaudible sounds by leveraging the spatial encoding capabilities of acoustic metasurfaces. Our design is grounded in the fact that, while sensitive to high-frequency signals, acoustic metasurfaces are almost non-responsive to low-frequency speech due to significant wavelength discrepancy. This observation allows us to integrate acoustic metasurfaces with sparse array geometry, simultaneously enhancing the spatial perception of high-frequency and low-frequency acoustic signals. To achieve this, we first utilize acoustic metasurfaces and a configuration optimization algorithm to encode the unique features for each incident angle. Then, we propose an unrolling soft thresholding network that employs neural-enhanced priors and compressive sensing for high-accuracy, high-resolution multi-source angle estimation. We implement a prototype, and experimental results demonstrate that MetaAng maintains robustness across various scenarios, facilitating multiple applications, including localization and tracking.
@article{MetaAng_Fu, author = {Fu, Yongjian and Zhang, Yongzhao and Pan, Hao and Lu, Yu and Li, Xinyi and Chen, Lili and Ren, Ju and Li, Xiong and Zhang, Xiaosong and Zhang, Yaoxue}, title = {Pushing the Limits of Acoustic Spatial Perception via Incident Angle Encoding}, year = {2024}, issue_date = {June 2024}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {8}, number = {2}, url = {https://doi.org/10.1145/3659583}, doi = {10.1145/3659583}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = may, }
IMWUT’24
UFace: Your Smartphone Can "Hear" Your Facial Expression!

Shuning Wang , Linghui Zhong , Yongjian Fu, and 3 more authors

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Feb 2024

Abs Bib

Facial expression recognition (FER) is a crucial task for human-computer interaction and a multitude of multimedia applications that typically call for friendly, unobtrusive, ubiquitous, and even long-term monitoring. Achieving such a FER system meeting these multi-requirements faces critical challenges, mainly including the tiny irregular non-periodic deformation of emotion movements, high variability in facial positions and severe self-interference caused by users’ own other behavior. In this work, we present UFace, a long-term, unobtrusive and reliable FER system for daily life using acoustic signals generated by a portable smartphone. We design an innovative network model with dual-stream input based on the attention mechanism, which can leverage distance-time profile features from various viewpoints to extract fine-grained emotion-related signal changes, thus enabling accurate identification of many kinds of expressions. Meanwhile, we propose effective mechanisms to deal with a series of interference issues during actual use. We implement UFace prototype with a daily-used smartphone and conduct extensive experiments in various real-world environments. The results demonstrate that UFace can successfully recognize 7 typical facial expressions with an average accuracy of 87.8% across 20 participants. Besides, the evaluation of different distances, angles, and interferences proves the great potential of the proposed system to be employed in practical scenarios.
@article{UFace_Wang, author = {Wang, Shuning and Zhong, Linghui and Fu, Yongjian and Chen, Lili and Ren, Ju and Zhang, Yaoxue}, title = {UFace: Your Smartphone Can "Hear" Your Facial Expression!}, year = {2024}, volume = {8}, number = {1}, url = {https://doi.org/10.1145/3643546}, doi = {10.1145/3643546}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = feb, }

2022

SenSys’22

SVoice: Enabling Voice Communication in Silence via Acoustic Sensing on Commodity Devices

Yongjian Fu, Shuning Wang , Linghui Zhong , and 3 more authors

In Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems , Nov 2022

Bib

@inproceedings{SenSys22_SVoice,
  author = {Fu, Yongjian and Wang, Shuning and Zhong, Linghui and Chen, Lili and Ren, Ju and Zhang, Yaoxue},
  title = {SVoice: Enabling Voice Communication in Silence via Acoustic Sensing on Commodity Devices},
  year = {2022},
  booktitle = {Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems},
  series = {SenSys'22},
  pages = {622--636},
  month = nov,
  numpages = {15},
  keywords = {acoustic sensing, {cGAN}, silent speech, transformer},
  location = {Boston, Massachusetts},
  publisher = {Association for Computing Machinery},
  url = {https://doi.org/10.1145/3560905.3568530},
  doi = {10.1145/3560905.3568530},
}